Inventory management and distribution of physical products

ABSTRACT

Among other things, product data is repeatedly communicated from an application on a device associated with a retail location to a server of a platform controlled independently of the application on the device. The product data identifies (a) current numbers of units of products available for retail customers at the retail location and (b) purchasing behavior of retail customers of the retail location with respect to combinations of the products. The retail location is repeatedly restocked with additional units of products to maintain the products in an in-stock state, the additional units have been received in deliveries made from one or more upstream product distribution entities based on predictions facilitated by the server of future demand for the products by retail customers of the retail location.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation application and claims priority under35 U.S.C. § 120 to U.S. application Ser. No. 16/212,538, filed Dec. 6,2018, the entire contents of which are incorporated here by reference.

BACKGROUND

This description relates to inventory management and distribution ofphysical products.

Distribution of physical products 12, 13 in a typical product market 10proceeds in stages through a distribution chain 14 from raw materials 16used in the manufacture of the products through delivery of the productsto end consumers 18, 20. The end consumers can be individuals 18 in thecase of retail products 13 or enterprises 20 in the case of commercialproducts 12.

The activities involved in product distribution can be performed,controlled, or managed by one or more product distribution entities 22,24, 26, 28 associated with the stages 30, 32, 34, 36 of the distributionchain. Examples of product distribution entities include: manufacturers22, distributors 24, wholesalers 26, and retailers 28, among others. Inthe case of retail products, the product distribution entity at thefinal stage of the distribution chain can be a retailer delivering (ormanaging, arranging, or facilitating the delivery of) the physicalproducts to individual end consumers.

SUMMARY

In general, in an aspect, product data is repeatedly communicated froman application on a device associated with a retail location to a serverof a platform controlled independently of the application on the device.The product data identifies (a) current numbers of units of productsavailable for retail customers at the retail location and (b) purchasingbehavior of retail customers of the retail location with respect tocombinations of the products. The retail location is repeatedlyrestocked with additional units of products to maintain the products inan in-stock state, the additional units have been received in deliveriesmade from one or more upstream product distribution entities based onpredictions facilitated by the server of future demand for the productsby retail customers of the retail location.

Implementations may include one or a combination of two or more of thefollowing features. The product data is communicated in response topolling. The application running on the device includes a POS system.The predictions have been based on the product data communicated to theserver. The predictions have been based on product data communicated tothe central server from applications on devices associated with otherretail locations. The deliveries have been made without any action havebeen required by the retail location. The units of the products aretransferred to retail customers without delay after purchase by theretail customers. The units of products are delivered within eight hoursafter they are ordered or purchased.

In general, in an aspect, an application on a device associated with awholesaler or distributor of retail products to retail locationsrepeatedly receives product data from a server of a platform controlledindependently of the application on the device. The product dataidentifies predictions of units of products to be delivered and timingof deliveries of the units of products to retail locations to maintainthe products in an in-stock state. The identified units are caused to bedelivered to the retail locations at times consistent with theidentified timing of deliveries.

Implementations may include one or a combination of two or more of thefollowing features. The application on the device includes a componentof an inventory management system. The predictions having been based onproduct data received by the server from the retail locations. Thereceived product data has identified (a) current numbers of units ofproducts available for retail customers at the retail locations and (b)purchasing behavior of retail customers of the retail locations withrespect to combinations of the products. In some implementations, thedelivery is caused without requiring prompting by the retail locations.

In general, in an aspect, product data is repeatedly received at aserver of a platform from devices associated with physical retaillocations. The product data identifies numbers of units of productscurrently available for retail customers at the physical retaillocations, the platform being controlled independently of the physicalretail locations. Product data stored at the server is repeatedlyupdated to reflect the repeatedly received product data. Pages areserved from the server through a communication network to web browsersof computing devices or apps of mobile devices. The pages identify unitsof products currently available at the physical retail locations. Thepages being are based on the updated product data stored at the server.

Implementations may include one or a combination of two or more of thefollowing features. The devices associated with the physical retaillocations include components of POS systems. The product data isreceived at the server without requiring action by the physical retaillocations.

In general, in an aspect, product data is repeatedly received, at aserver of a platform, from devices associated with product distributionentities. The product data has aspects confidential to the productdistribution entities. The platform is controlled independently of theproduct distribution entities. The product data, including theconfidential aspects, is stored at the server. Access is controlled toprotect the confidential aspects of the product data. The product datais processed to produce derived information about a product distributionchain for the products. The derived information does not reveal theconfidential aspects. The derived information is communicated toparticipants of the platform.

Implementations may include one or a combination of two or more of thefollowing features. The processing of the product data includespredicting demand for the products. The processing of the product dataincludes aggregating the data to produce aggregated data free of theconfidential aspects. The processing of the product data includesanalyzing consumer demand behavior. The processing of the product dataincludes analyzing supply. The storing of the product data includestranslating the product data to a canonical form and storing thecanonical form. The communicating of the derived information includestranslating the product data from the canonical form to proprietaryforms used by the participants of the platform. The receiving of productdata from devices associated with product distribution entities includesreceiving product data from product distribution entities at two or morestages of a product distribution chain. The receiving of product datafrom devices associated with product distribution entities includesreceiving product data from product distribution entities that arecompetitors at a given stage of a product distribution chain. Theproduct data includes transactions in which products are sold by productdistribution entities to end consumers.

These and other aspects, features, implementations, and advantages (1)can be expressed as methods, apparatus, systems, components, programproducts, business methods, means or steps for performing functions, andin other ways, and (b) will become apparent from the followingdescription and from the claims.

DESCRIPTION

FIGS. 1 through 7 are block diagrams.

HOW PRODUCT DISTRIBUTION WORKS Stages of the Distribution Chain

Each of the stages in the distribution chain for a product market ischaracterized by its association not only with one or more productdistribution entities, but also with product storage facilities 40, 42,44, 46 (for example, a warehouse), a geographic distribution location50, 52, 54, 56 of each product storage facility, and a distributionmanagement technology 60, 62, 64, 66, among other things.

Distribution management technology can include computer-based systems tofacilitate a variety of activities including the ordering of productsfrom product distribution entities at upstream stages, the receipt andplacing of received physical products into inventory at the productstorage facility, the tracking and maintenance of the inventory in theproduct storage facility, the receipt and fulfillment of orders forphysical products from product distribution entities at downstreamstages, the analysis and prediction of demand by downstream productdistribution entities or end consumers, and the analysis and predictionof availability of products from upstream product distribution entities(e.g., vendors or suppliers). Each product storage facility can includeor be associated with capabilities for receiving, handling, storing,counting, safeguarding, and shipping physical products, among otheractivities.

Delivery

The complete distribution chain for a particular product or productmarket includes not only the product distribution entities at successivestages, but also product delivery entities delivering 70, 72 thephysical products from each upstream product distribution entity to adownstream product distribution entity. The delivery can be performed byproduct distribution entities themselves or by third-party contractors,for example.

In some product markets, each stage of the distribution chain caninclude many competitor product distribution entities (not shown).Physical products can follow different paths (not shown) throughdifferent product distribution entities of the distribution chain ontheir way to the end consumers.

Taxonomy

In some instances, physical products distributed through thedistribution chain can be identified using a hierarchy (or taxonomy) ofdescriptive identifiers. For example, within a particular distributionchain or product market, physical products can be identified by productcategory, such as film cameras, digital single-lens-reflex cameras, anddigital point-and-shoot cameras. Within each product category, productscan be identified by product manufacturer, such as Canon, Nikon, andLeica. For each product manufacturer, products can be identified byproduct model number, such as Nikon D850 or Nikon D7500. Within eachmodel number, individual units of a product can be identified by, say, aproduct serial number. Other taxonomies, ontologies, and hierarchies mayapply in other product markets. Distribution of physical productsthrough the distribution chain of a product market is often tracked andmanaged in terms of a number of product units of a given product,manufacturer, and model number for purposes of shipping, ordering, orderfulfillment, and inventorying. For example, a camera distributor mayreceive an order for 50 units of Nikon D850 digital single lens reflexcameras. We sometimes use the term “product” to refer to any of thegroupings of products within a level or combinations of levels of thehierarchy. We sometimes use the term “units” to refer to individualproduct units.

Such hierarchies of descriptive identifiers can be useful in acquiring,maintaining, organizing, processing, analyzing, and communicating dataabout product distribution activities and in presenting informationabout products to customers.

Inventorying

Typical product markets and distribution chains are characterized byinefficiencies in the flow and inventorying of products. Because ittakes time for delivery of products from stage to stage in adistribution chain and because larger than ideal levels of inventory mayneed to be maintained at one or more stages, moving products through thedistribution chain can add to the cost and reduce the profits of productdistribution entities along the distribution chain.

Among other expenses, each product distribution entity must devotecapital to the inventorying of products in its product storagefacilities. The capital required for inventory depends not only on thenumber of products and the number of units of each product stored, butalso on the amount of time that each of the units of each productremains in a product storage facility.

Because the cost of capital impacts profitability, each of the productdistribution entities aims to minimize the number of units of eachproduct held in product storage facilities and the length of time eachof the units is held. A key step for a product distribution entity tominimize inventory costs is to match supply from upstream stages todemand from downstream stages of the distribution chain. Demand caninclude the current demand and expected demand for products fromdownstream product distribution entities or end consumers. Supply caninclude the current supply and expected supply of products or rawmaterials from upstream stages of the distribution chain.

If the product distribution entity can accurately predict supply anddemand, it can take steps to match them. Yet supply and demand are hardto predict and subject to variations over time (e.g., unexpected ordersor large orders). As a result, a product distribution entity may need toinventory more products and more units of products (safety stocks) thanwould otherwise be needed as a buffer against unexpected higher demandor unexpected lower supply. Failing to meet demand can impact anentity's reputation, create a negative customer service experience, andresult in lost future business. The capital cost of safety stocks canextend to rental costs for additional warehouse space to accommodate thesafety stock. In addition, if products in the safety stock have limitedshelf lives, units of product in the safety stock could reach an expirydate or be superseded by new models or versions of the product or newproducts, forcing the product distribution entity to discard or destroythem.

To predict and match supply and demand accurately, product distributionentities need accurate, complete, and current product data.

Distribution Management Technology

The distribution management technology owned, controlled, or managed byeach product distribution entity can, among other things, receive,store, maintain, analyze, and provide product data (sometimes simply“data”) associated with products received, inventoried, and sold by theproduct distribution entity and with the entity's role in distributionof the products. For example, the product data can include productsordered, products in inventory, products shipped, predicted productdemand, predicted product supply, and historical product data, among awide variety of other data items.

Product data can have great value to a product distribution entity andprovide a competitive advantage in the product market, for example.Therefore, depending on the product market and the product distributionentity, a product distribution entity may keep its product data secretfrom its competitors and other parties. In some cases, however, limitedproduct data may be shared between two product distribution entitiesdoing business with one another at successive stages of the distributionchain. For example, a retailer may share with a wholesaler product dataindicative of the retailer's predictions about future end consumerdemand for particular products so that the wholesaler can serve theretailer more effectively.

Inventory Management Systems

As part of their distribution management technology, manufacturers,wholesalers, distributors, retailers, and other product distributionentities use a variety of inventory management systems and components ofthem including: Material Requirements Planning (MRP) systems, EnterpriseResource Planning (ERP) systems, Warehouse Management System (WMS)systems, and Point of Sale (POS) systems, for example. The inventorymanagement systems, components of them, and combinations of the systemsand components differ with respect to different stages of thedistribution chain.

MRP systems help manufacturers calculate what raw materials they need,when, and in what quantities. MRP II systems are a more recent versionof MRP and include detailed capacity planning, scheduling, shop floorcontrol, and other calculations. MRP II enables manufacturers to compareforecast product data with actual product data and analyze performanceand improve processes.

ERP systems are typically suites of integrated applications that anentity can use to collect, store, manage, and interpret product datafrom departments of the organization, such as purchasing, sales,manufacturing, human resources, services, and inventory.

WMS systems help manage day-to-day operations in a warehouse. WMSsoftware guides inventory receiving and put-away, optimizes picking andshipping of orders, and advises on inventory replenishment. A warehousemanagement system can be a standalone application or part of an ERPsystem, among other implementations.

POS Systems

POS systems, which are typically used by product distribution entitiesthat are retailers, are combinations of hardware and software built tocentralize business operations. POS systems can manage the transactionsof a business (including credit card processing and the post-transactionoperations that lead to fulfillment of the transactions), store,maintain, and update product data as transactions are processed, trackinventory and sales (and other product) data at individual retaillocations, and aggregate inventory and sales data for multiple retaillocations owned or controlled by a particular retailer, for example.

POS systems are usually proprietary; the product data that they generateand maintain is not shared between separate retail locations orretailers. Typical proprietary POS systems are not built to cooperatewith other POS systems. Competing developers of POS systems arereluctant to provide integration features or to enable them to shareproduct data. Many POS systems use proprietary data formats, databaseschemas, and communication protocols making it challenging for differentPOS systems to share product data and communicate with one other.

In addition to being incompatible with product data of other POSsystems, the product data generated by a proprietary POS system may notbe complete, accurate, or current. And product data often is storedsecurely with limited access only to a single retail location or asingle retailer who owns or controls the POS system.

At other stages of a distribution chain, the product distributionentities also use systems that has similarities to POS systems to trackproducts and generate product data. Such systems often have features andshortcomings similar to those of POS systems at the retail level.

Product data is typically stored in databases controlled by productdistribution entities for their own benefit. The databases aremaintained and used by components of the distribution managementtechnology. Each of the databases is typically segregated for eachcomponent and access is limited to authorized representatives of theproduct distribution entity using unique usernames and passwords validonly for that database and for a single component. Different productdistribution entities that make use of product data applications anddatabases often are assigned separate databases and accounts perapplication to mitigate the risk of unauthorized access across theinstances of the applications (and therefore between unrelated productdistribution entities).

Flow of Products and Collection of Product Data through the DistributionChain

As products move from stage to stage through a distribution chain, theproduct distribution entities use their inventory management systems totrack the products and the activities associated with their distributionand generate and store corresponding product data.

For example, an inventory management system can track the movement ofraw materials to the production line at a manufacturing facility.Product data then is updated to show that the raw materials stock in thewarehouse has been reduced and the raw materials available to supportproduction have increased. When the products are moved from theproduction line to the warehouse, the inventory management system isupdated to show how many units of product were produced, when they weremanufactured, and where they have been stored awaiting distribution.

Once the manufacturer receives a purchase order from a wholesaler ordistributor, the manufacturer will remove the ordered products from thelocal warehouse, package them, and prepare them to be transported to thepurchaser. Then the manufacturer will update its inventory managementsystem to reflect the reduction in inventory and the fact that theproducts are being delivered to the customer's distribution location.

When received, the wholesaler will inspect the products for qualitycontrol, ensure they match what was ordered, enter them into itsinventory management system, and move them to a defined storage locationin its warehouse. The wholesaler will hold the products in its warehouseuntil it receives an order from one of its downstream retailers or otherdownstream product distribution entities. This order will typicallyarrive as a purchase order detailing products the retailer wants to buy,the number of units of each product, and when and where the orderedproducts should be delivered. These activities by the wholesaler can beentered as product data into its inventory management system.

A purchase order may cover products that originate from severaldifferent manufacturers or may be for products that come from a singlemanufacturer. In the first scenario, the wholesaler or distributor willwithdraw the products requested from its inventory, combine them into asingle shipping unit and prepare it for delivery. As the products areremoved from inventory the inventory management system will be updatedto show a reduction in inventory and a corresponding increase in theinventory that has been assigned to that purchase order. When the orderis ready, the wholesaler or distributor will arrange for delivery to theretail location. When the products leave the warehouse for delivery, theinventory management system is again updated.

At the final stage of the distribution chain in the context of retailproducts, retailers place orders with wholesalers and distributors forproducts and numbers of units of each product for stocking in theirretail locations to serve expected demand of end consumers. When theretailer receives ordered products at its warehouses or its retaillocations, the retailer inspects them to ensure that they match thepurchase order and the level of quality meets expectations, then updatesits inventory management system to show the increase in stock(inventory).

In some cases, rather than initiating purchase orders, the retailerwaits for the wholesaler's or distributor's representative to visit theretail location in a sales call. The visit may include an introductionof new products to the retailer. And the wholesaler's, distributor's, ormanufacturer's representative may take stock of the retailer's inventoryto determine what products and how many units of each need to bere-ordered. Employing such representatives can be a significant expenseof the wholesaler, distributor, or manufacturer.

The product distribution entities in the distribution chain thereforeare continually generating a large volume of detailed product data,although much of it is never shared with other parties. At the sametime, there can be significant gaps in the product data generated in thefirst place, for various reasons. Different product distributionentities will vary widely in the sophistication of their inventorymanagement systems. The inventory management systems will also vary intheir capabilities for collecting product data from rudimentary tohighly sophisticated. In some cases, the collected product data will beincorrect or incomplete or lost.

Optimizing Inventory The Theory

As explained above, to minimize their costs and maximize their profits,retailers, manufacturers, wholesalers, distributors, and other productdistribution entities aim to maintain optimal inventory levels at theirretail locations.

In theory, each product distribution entity could minimize (reduceessentially to zero) the number of products, the number of units of eachproduct, and the period during which each unit is stored, if the entityalways possessed or could access complete, accurate, and current productdata of other product distribution entities at the same stage ordifferent stages of the distribution chain. Such exhaustive product datawould enable the entity to determine or predict the current andanticipated demand for every product and for units of each product fromdownstream stages. The product data would also enable the entity todetermine or predict the current and future availability of products orraw materials from upstream stages including amounts of delay to beexpected currently and in the future in the receipt of products or rawmaterials. In such an ideal scenario, essentially no unit of productwould need to be stored in inventory, because each unit of each productwould arrive at each product storage facility at the exact time neededfor shipping to the downstream customer.

The Reality

In reality, the optimal inventory situation is unreachable. Onesignificant reason is that exhaustive product data is not available tothe product distribution entities in the distribution chain.Accordingly, it becomes impossible or difficult for inventory managementsystems to generate useful analyses and predictions of demand and supplyand a variety of other features of the product distribution process. Theresult is significant inefficiencies in product distribution, highercosts, and lost profits, among other disadvantages.

Techniques to Reduce Inventory

Nevertheless, useful techniques are available for inventory management,notably techniques based on predicting product demand and productsupply. For the purpose of making such predictions, product distributionentities often use simple or sophisticated mathematical predictivemodels of portions of the distribution chain that are most relevant tothem. A wide variety of modeling techniques are used in the resultingpredictions may vary widely in their reliability and accuracy.Predictive models of aspects of the distribution chain tend to be moreaccurate and more useful when the quantity of product data (current andhistorical) is large, complete, and accurate. To the extent that in agiven distribution chain, the product data is sparse, incomplete,outdated, or inaccurate, the predictive models suffer in their abilityto provide useful predictions.

Other inventory reduction techniques are possible.

In some cases, an upstream product distribution entity (for example, amanufacturer) can work with a product distribution entity at thedownstream stage (for example, a wholesaler or distributor) tounderstand its expected demand for an upcoming period (which could be ayear or more depending on the product). Then the product distributionentity can determine what to manufacture, how much of each product toproduce or stock, when to produce it or stock it, and how much to holdin its inventory.

Once a product distribution entity understands the likely future demandfor its products and the ability of the manufacturer or other upstreamproduct distribution entity to supply products to meet that demand, theproduct distribution entity and the downstream product distribution canenter into a purchase order or contract that defines what is going to beordered and when. With this in hand, the upstream product distributionentity can plan its operation efficiently. For example, a manufacturercan buy the right amount of raw material when needed to manufacture theselection of products and the number of product units of each productexpected to be in demand.

In some cases, the efficiency of the flow of raw materials and productsthrough a distribution chain depends on the degree of verticalintegration in the product market, the degree of fragmentation of theentities in the product market, the nature of the products being sold,and end consumers' purchasing behaviors and expectations.

If a supplier and a downstream customer (e.g., wholesaler and retailer)have worked together for many years they are more likely to share salesand inventory product data. However, if the industry is fragmented andthe downstream customer changes suppliers frequently, sharing of productdata is less likely. This reluctance to share product data hinders thesupplier's effort to develop accurate demand forecasts and makes it morelikely that the supplier will be unable to meet its customer's demands.

As an example of vertical integration, product distribution entities inthe automobile industry have worked to integrate their operations andshare data to improve their efficiency and profitability. The shareddata allows relatively accurate predictions of supply and demand at eachstage enabling a product distribution entity to plan what it is going toproduce or order and what it is going to supply and sell.

Conversely, the clothing industry is fragmented among manymanufacturers, distributors, wholesalers, and retailers often operatingin isolation and competing against each other. Clothing distributionentities are reluctant to share product data for reasons including highrates at which manufacturers and retailers are formed or go out ofbusiness, and the low margins characteristic of clothing distributionentities. Therefore, flow of raw materials and products through thedistribution chain is not efficient.

In addition to predicting demand, other inventory management processescan be applied to manage inventories including production planning,fine-grained tracking of the location and status of raw materials andproducts, and just-in-time receipt of raw materials or products fromupstream suppliers, to name a few.

The value of a supplier's customer demand model depends on thecompleteness, accuracy, and timeliness of product data from customers.Having such product data turns on the customers' willingness to sharetheir proprietary product data and the supplier's ability to collect itand interpret it. This is a challenging endeavor if the lead time forthe products that it manufactures or supplies is long. The product datamay have to be collected months or years before the customer will needthe product, yet product data collected early is more likely to beincorrect or outdated.

A product distribution entity may not be able to accurately forecast itscustomer's demand due to: lack of information, poor planning, seasonalvariations, industry fragmentation, inexperience, lack of an adequateinventory management system, lead time, delivery variability, supplierperformance or reliability, and other factors, and combinations of them.

Universal Inventory Management and Product Distribution Platform

As shown in FIG. 2, here we describe a technology that we sometimesrefer to as a “universal inventory and product distribution platform”100 or a “universal platform” or simply a “platform.” The universalplatform 100 can significantly improve the efficiency, profitability,speed, and effectiveness of each product distribution entity (and theaggregation of product distribution entities) at each stage of thedistribution of physical products through a distribution chain from rawmaterials used in the manufacture of the products through delivery ofthe products to end consumers. These improvements can be achieved as aresult, for example, of more accurate, timely, and complete product databeing accumulated, stored, analyzed, and made available to and amongproduct distribution entities and other participants by the universaldistribution platform. The improvements can benefit individual productdistribution entities, pairs and groups of such entities, and in somecases an entire product market or distribution chain. The improvementscan be used to provide new and beneficial applications and capabilitiesfor distribution chains, product markets, product distribution entities,delivery entities, and other participants in the platform.

The platform can enable every product distribution entity at every stageof the distribution chain to, among other things, minimize its inventoryand optimize the ordering, inventorying, and shipping of products at alltimes. When the technology is applied by multiple product distributionentities in the distribution chain, the overall efficiency of thedistribution chain will improve. The technology is applicable to thedistribution chain for every kind of physical product and for every kindof vertical or general product market for physical products and forevery geographic context.

Some implementations of the platform apply to the process of supplyingproducts from manufacturers in a product market (and potentially acrossproduct markets) through distribution chains to retail locations and toretail end consumers using a network of product distribution nodescontrolled by respective product distribution entities.

As illustrated in FIG. 1, typically each product distribution node 80(or simply “node”) has an inventory facility (e.g., a warehouse), one ormore portals to receive products at the inventory facility from upstreamproduct distribution entities (suppliers), one or more portals (whichcould be the same as portals) from which to deliver products from theinventory facility to downstream customers, product distributionmanagement technology that includes inventory management systems (e.g.,inventory management system 82) and other systems, to manage the flow ofproducts into the portals and out of the portals and the maintenance ofproducts in the inventory facility, among other things.

In effect, the products held at product distribution nodes and productsbeing delivered between nodes of the distribution chains in a givenproduct market represent the aggregate total current global inventoriesof products of that product market. From the perspective of the overallproduct market, the total inventories of products held in the globalinventories should be as small as possible, ideally essentially zero andthe time required to deliver the products from manufacturer to endconsumer should be essentially zero. In such a scenario, at each momentwhen an end consumer seeks to buy a product, the manufacturer couldproduce it, and have it delivered to the end consumer instantaneously.

To the extent that, in real-world situations, delays occur inmanufacturing, ordering, and delivery, inventories must be maintained toaccommodate such delays and uncertainties in demand and supply.Corresponding costs are incurred that reduce the profitability of theproduct distribution entities and the product market as a whole. Theprofitability of the product distribution entities for a product marketas a whole could be optimized by minimizing the number and length ofdelivery delays and minimizing the number of units of productinventoried by each product distribution entity individually and all ofthe product distribution entities taken as a whole.

In any product market, each product distribution entity strivesindependently to minimize the number of products held in its inventoryfacility and the delivery delays and to increase the demand for productsby its customers by providing good service. The platform enables eachproduct entity to achieve these goals by causing product data thatotherwise would not be collected or would be kept secret by each of theentities to be collected and made available to other entities in thedistribution chain. The platform can help to assure that current,complete, and accurate product data is readily available, accurate,timely, and complete.

The platform facilitates the collection of complete, accurate, timelyproduct data from potentially all of the product distribution entities,delivery entities, and other parties associated with the distributionchain (that is, all of the participants in the platform).

Hosting Party, Servers

For this purpose, the platform is hosted by a hosting party 102 that, insome implementations, is not (and is known not to be) a productdistribution entity, a delivery entity, or a participant in any otherrole in the platform. In a sense, the hosting party is understood to bean “honest broker” of product data and not a party that can itselfbenefit directly from the use of product data for purposes of productdistribution. The hosting party, of course, can derive revenue fromparticipants in exchange for services that it performs and informationthat it provides through the platform to participants.

The platform can be hosted on a central server or servers 104 that arelocated at the site of the hosting party or are hosted by a cloudservice or a combination of those approaches. Software running onhardware (including processors) at the central server or servers caninclude at least the following components: a participant accountcomponent 106, a product data acquisition and delivery component 108, aproduct data translation component 110, an analytical component 112, aweb server component 114, a confidentiality enforcement component 116, aproduct data aggregation component 118, a database component, acommunication component 120, a communication component 122, and othercomponents. Additional information about these components is providedlater.

A wide variety of applications are possible for the platform across arange of product markets, distribution chains industries, stages ofdistribution chains, and product types. The applications can be inmarkets for commercial products and commercial consumers and indifferent stages (such as, manufacturers, distributors, or wholesalers)of the distribution chains for those markets.

Product Data

The hardware and software of the platform is structured and configuredto enable any participant 150 to submit product data at any time, fromtime-to-time, regularly, or on demand to the platform for storage in thedatabase component 120. We sometimes refer to such data as submittedproduct data. The platform also enables authorized participants tosearch for and access submitted product data (subject to security,anonymization, and aggregation techniques described below) at any time,from time-to-time, regularly, or on demand. We sometimes refer to suchdata as accessible product data. Therefore, the platform serves as aclearing house for product data.

Submitted product data and accessible product data can includeinformation about products that have been ordered from upstream productdistribution entities, are being delivered, have entered inventory, arein inventory, have left inventory, or have been ordered by downstreamproduct distribution entities, among other things. Submitted productdata and accessible product data can include product transaction dataabout orders, purchases, and sales of products. Submitted product dataand accessible product data can be expressed at any level of the producthierarchy, for example, using descriptive identifiers associated withproduct categories, product manufacturers, product model numbers,product serial numbers, and with respect to individual product units.

Submitted product data and accessible product data can be expressed asproduct data records. Each of the product data records can include oneor more of the following product data fields or other product dataitems: descriptive identifiers for a product expressed at any level ofthe hierarchy, number of units of a product, a date and time of thetransaction or event related to the product, status of a product (e.g.,in inventory, in delivery, ordered, in process, subject to pendingorder, subject to predicted order), location, price, discount, orcommission, for example. A product data record could include any otherdata field or other product data item that is generated or owned,controlled, or managed by any participant in the platform or would beuseful to any participant in the platform for purposes of ordering,delivering, or inventorying, managing any of those activities, modeling,or any other activity that improves the efficiency of any stage of thedistribution chain, or combinations of those activities.

Product data and product data records generated, or owned, control, ormanaged by any participant were stored and used at the central servercan be accumulated, total, or otherwise aggregated to form aggregatedproduct data or aggregated product data records for any of theactivities noted above. The aggregation can be across productdistribution entities at any given stage of the distribution chain oracross two or more stages or both. Although each product data record canbe associated with a particular time or a particular geographic locationor both, product data records and product data can be aggregated over aperiod of time or across two or more geographic locations or both.

In some cases, submitted product data records can be protected by a widevariety of encryption, secret sharing, or other security techniques andcombinations of them.

The Virtuous Cycle of More Data and Better Insights

Participants in the platform often will be providing submitted productdata considered secret and valuable by the participants, and versions ofthat submitted product data may be made available to other participants,for example, as aggregated product data or aggregated product datarecords. Aggregating of product data and product data records can havean advantage of hiding or masking specific product data records have aparticular participant, just as aggregating data about high net worthindividuals living in a particular city can be used to mask the identityof any particular high net worth individual. Nevertheless, even inaggregation, product data records may provide information useful toanother participant. Furthermore, in some uses of the platform,submitted product data records are not aggregated and not hidden and yetmay be provided to and useful to other participants in the platform. Thedisclosure of such submitted product data records carries risks.

In exchange for its risk of disclosure of its own product data recordseven in an un-aggregated form, a submitting participant receives thebenefit of being able to access product data in product data records(including unmasked product data records and aggregations of productdata records) generated or controlled and submitted to the platform byother participants including the first participant's competitors,suppliers, and customers. As additional value in exchange for disclosingits own product data, the participant can also receive valuable analysisand insights about supply, demand, prices, and other informationassociated with product data and with the product market in which itparticipates.

Notwithstanding these benefits of participation, participants need to beconfident in the willingness, commitment, and ability of the platform(and the hosting party) to safeguard their product data, to use indistributed only as agreed, and to continually improve the analyses andinsights provided back to the participants. Once the platform hasdemonstrated such willingness, commitment, and capability, itsreputation for providing useful information within a product marketwhile protecting participant's information will attract moreparticipants and the submission of more complete, accurate, and timelyproduct data from existing participants. The additional product data andadditional participants in turn enable the platform to improve thecomprehensiveness of product data that it has available, can aggregate,and report, and the quality of the product data, aggregations, analyses,and insights that it makes available. The resulting cycle is virtuous.Among other things, the improved platform may be able to keep costs toparticipants low.

An important aspect of the building and maintaining of confidence of theparticipants in the platform may, in some cases, be its ability toprotect the confidentiality of the product data of participants,anonymize fields of the product data, aggregate fields of the productdata while excluding identifiable confidential information, and controlthe access of participants to product data maintained by the platform.

Participant Hardware and Software

As shown in FIG. 3, in addition to the hardware and software hosted bythe hosting party at the central server, the platform can include one ormore of the following components running (for example, as part of aparticipant's inventory management system 154 in the case of aparticipant that is a product distribution entity) on hardware 152owned, controlled, or operated by one or more participants 150: aninventory management system coordination component 156, a product dataacquisition and delivery component 158, a product data translationcomponent 160, a platform coordination component 162, an analyticalcomponent 164, a product data aggregation component 166, a databasecomponent 168, a communication component 170, the confidentialityenforcement component 171, a web browser component 172, and othercomponents.

The particular components running on the hardware of the differentparticipants can vary widely. When the participant is a productdistribution entity, components will generally include at least theinventory management system coordination component, the product datatranslation component, the platform coordination component, and thecommunication component. For some product distribution entities that useinventory management systems having limited functionality, thecomponents of the platform running on the participant's hardware couldalso include at least the product data acquisition and deliverycomponent and the product data aggregation component. When theparticipant is not a product distribution entity (for example, athird-party supplier or user of product data), the components running onthe participant's hardware could include at least a product datatranslation component, a platform coordination component, an analyticalcomponent, a product data aggregation component, a database component,and a communication component. Other combinations are also possible.

Components of the platform running on the central server and componentsof the platform running on equipment of the participants can communicatewith one another through any communication network such as the Internet.

Certain components may be run on either or both of the platform serverand the participant's hardware including, for example, a product datatranslation component, an analytical component, a product dataaggregation component, or a database component. In some cases, thefunctions performed by these components can be performed cooperativelybetween components running on both the server and the participant'shardware. In some instances, the functions can be performed either onlyat the platform server or only at the participant's hardware.

Therefore, in some implementations, some functions of the platform canbe performed cooperatively between components running on the centralserver and components running on the participant's hardware. Forexample, product data received, stored, and used by the platform can bemaintained as records in tables of a database managed by a databasecomponent 120 (at the central server) or a database component 168 (atthe participant's hardware). Maintenance and management of such adatabase can be handled solely at the server, solely at theparticipant's hardware, or at a combination of the server and theparticipant's hardware. A variety of analytical functions and theoutputs of the functions can be provided by the platform either solelyby the analytical component 112 running at the server, solely byanalytical components 164 running at the participants, or by acombination of analytical components running at the server and theparticipants. The performance of a given analytical function can besplit. The function of aggregating product data also can be performedsolely by a product data aggregation component 118 at the central serveror a product data acquisition and delivery component 166 at theparticipant's hardware, or at a combination of the central server andthe participant hardware. Techniques for controlling access tocomponents of the platform running on the central server and on theparticipant's hardware can be shared by a participant account component106 or similar component at the participant hardware or can be performedprimarily on one or the other. Similarly, the protection ofconfidentiality of product data fields can be controlled either by theconfidentiality enforcement component 116 at the central server or aconfidentiality enforcement component 171 at the participant hardware ora combination of both.

Product data acquired or stored in the inventory management systems ofproduct distribution entities are typically expressed in a variety ofproprietary formats that are incompatible with one another. The platformdefines a common or canonical protocol for expressing product dataacquired or stored at any participant or maintained or used for anypurpose at the central server. Translations of product data back andforth between proprietary protocols used by proprietary inventorymanagement systems and the common or canonical protocol used in somefunctions of the platform are performed by the product data translationcomponents 110, 160 running at the central server or the participanthardware.

Here we describe certain functions and relationships among thecomponents of the platform in more detail.

Central Server Communication Component

The communication component 122 provides the features necessary oruseful for the central server to communicate with any type ofparticipant or user through a communication network such as theInternet. The communication component 122 is configured to be able tofunction with any type of communication protocol including cellular andother wireless protocols. The communication can be made directly betweenthe communication component 122 and a corresponding communicationcomponent in the participant hardware using typical Internet protocols.In some implementations, some of the communication between the centralserver and the participant can include use of the Web server component114 at the central server and a corresponding Web browser at theparticipant. In such cases, the Web server component and the Web browsercomponent can communicate through the corresponding communicationcomponents.

Central Server Web Server Component

The Web server component 114 serves webpages through the communicationcomponent to the participant Web browser using, for example, productdata stored in the database component 120 for product data provided bythe product data acquisition and delivery component, or a combination ofboth. The Web server component 114 also can provide data to the productdata acquisition and delivery component for processing there. The Webserver component communicates with the participant account component tomanage the authentication process and changes to participant accounts,for example. The Web server component also interacts with theconfidentiality enforcement component 116 for purposes explained below.

Central Server Participant Account Component

The central server participant account component 106 maintains accountsfor participants and individuals representing participants, includingproduct distribution entities and other participants. Participants andindividual users can maintain profiles and authentication credentialsusing this component. When a participant or individual user logs inthrough a web browser and is authenticated, the participant orindividual user is given access to authorized portions of the databasecomponent and functions provided by other components running on thecentral server, such as the analytical component. The participantaccount component also interacts with the confidentiality enforcementcomponent to provide controls, based on the participant account andparticipant profile, to protect the confidentiality of certain kinds ofparticipant information and product data.

Central Server Product Data Acquisition and Delivery Component

The product data acquisition and delivery component 108 managesinteraction through the communication component 122 with the participant150 for the purpose of acquiring product data that has been stored or isbeing generated at the participant hardware for the purpose ofdelivering product data to the participant that is been stored in thedatabase component 120 and processed by various components at theserver. Product data acquisition and delivery can be done by the productdata acquisition and delivery component polling or being polled by theparticipant hardware periodically for product data in order to keep theproduct data at the central server or the participant, or both, current,complete, and accurate. In some implementations, the participanthardware actively sends product data to the product data acquisition anddelivery component periodically without being polled. Similarly, theserver can actively send product data to the participant without beingpolled. Delivery and receipt of product data can also occur at any timeas product data changes or is generated at the participant hardware oris processed and stored in the database component at the server. In somecases, batches of product data can be delivered to or received by theproduct data acquisition and delivery component. This could occur notonly in connection with polling, but also at particular times when a newparticipant has been added to the platform or has made significantchanges in its hardware or its approach to gathering, processing, anddelivering product data, or when the server has acquired volumes ofadditional product data or has changed its approach to processing theproduct data.

Product data acquired by the product data acquisition and deliverycomponent can be stored in the database component 120 in the form inwhich the product data is received, without further processing at thecentral server. In some instances, the received product data can insteador additionally be delivered to the product data translation component110.

Central Server Product Data Translation Component

The product data translation component 110 can receive product datadirectly from product data acquisition and delivery component 108 orfrom the database component 120. The received product data can be, forexample, raw product data received from the participant hardware anduntranslated according to a canonical format of the kind discussedlater. The product data translation component 110 can perform thetranslation of received product data or product data stored in thedatabase component directly and completely into the canonical format orcan perform a partial translation to be completed a later time. Theresult of the operation of the product data translation component willbe fully or partially translated product data that is then stored in thedatabase component. For some purposes, the product data translationcomponent 110 can perform translation from the canonical formatpartially or fully back to a raw (e.g., proprietary) data format used bya participant's hardware.

Central Server Product Data Aggregation Component

For a variety of purposes and uses, including protecting theconfidentiality of particular product data of particular participants,the product data aggregation component 118 manages the accumulation,assembly, and compilation of product data across a variety of differentparameters. For this purpose, the product data aggregation componentfetches product data from the database component 120, aggregates it inaccordance with relevant aggregation rules based on the parameters, andreturns aggregated product data the database component for storage andsubsequent use in delivery to participants. The parameters according towhich aggregation can be done can include time periods, geographicalareas, product categories, product manufacturers, product model numbers,product serial numbers, product units, stages of product distribution inthe product distribution chain, groupings of product distributionentities, and other parameters, and combinations of them. Aggregationcan span two or more of those parameters.

Central Server Database Component

The database component includes a database of tables of product data andtables related to participant accounts, among other things, and adatabase engine that enables reliable and secure storage and fetching ofproduct data. The records and fields of the product data tables can beexpressed in a wide variety of forms and formats. The stored productdata records can include records expressed in raw or proprietary productdata formats, such as formats used by participant hardware, recordsexpressed in accordance with the canonical format, and records expressedin partially translated formats, or records expressed in other formats,or combinations of them. The stored product data records can alsoinclude records expressed in a wide variety of aggregations based on theparameters described above.

Central Server Analytical Component

The analytical component provides a wide variety of analytical devices,tools, algorithms, and approaches for analyzing, summarizing,manipulating, processing, and otherwise interpreting product data storedin the database. The analytical component can be configured and used byparticipants in participant users through web communications and also byusers associated with the server or the hosting party through a userinterface provided by the server. Among other things, the analyticalcomponent can be used to develop, train, and apply models, algorithms,and other analytical tools for understanding, recording, interpreting,and predicting inventories, demands, supplies, consumer behaviors,behaviors of product distribution entities and groups of them, behaviorsof product distribution markets and sub- markets, prices and pricing andvalues of product units, product models, product manufacturers, timeperiods, geographical areas, and other parameters and factors useful toproduct distribution entities and other participants.

Central Server Confidentiality Enforcement Component

The confidentiality enforcement component of the central server managesfunctions that assure that product data of product distribution entitiesand other participants is treated in accordance with understandings andagreements with those entities and participants including with respectto confidentiality, aggregation, and security. As explained below, trustby product distribution entities and other participants of the servers,the platform, and the hosting party is an important feature of theoperation of the platform, not only to protect against claims thatproduct data has been improperly used or disclosed, but also as animportant feature of the building of a comprehensive, complete,up-to-date, and accurate set of product data in the database.

Participant Components

In addition to the components running on the server, the platform caninclude components running on each of the participant's hardware. Thesecomponents can be generated by the hosting party of the platform or bythe participant or by a combination of them. Components that aregenerated by the hosting party can be distributed to the participantselectronically and installed on the participant hardware by participantusers or representatives of the hosting party. Generally, in addition tothe other functions performed by these participant components (describedbelow), they serve to provide an interface or conduit betweenproprietary systems typically used on participant hardware and theshared systems operating on the servers. In this respect one or more theparticipant components can have server-facing aspects and featuresconfigured to interact correctly with corresponding components in theserver and participant-facing aspects and features configured tointeract correctly with proprietary and other components, software,features, and aspects of inventory management systems and other systemsoperated by the participant on the hardware.

Participant Communication Component

As shown in FIG. 3, the communication component 170 of the inventorymanagement system of the participant's hardware provides the featuresnecessary or useful for the central server to communicate with theserver through a communication network such as the Internet. Thecommunication component 170 is configured to be able to function withany type of communication protocol including cellular and other wirelessprotocols. The communication can be made directly between thecommunication component 170 and a corresponding communication componentin the participant hardware using typical Internet protocols. In someimplementations, some of the communication between the participant andthe central server can include use of a web browser component 172interacting with the web server component 114 at the central server. Insuch cases, the Web server component and the Web browser component cancommunicate through the corresponding communication components.

Participant Web Browser Component

The Web browser component 172 receives webpages through thecommunication component 170 from the Web server at the central server.The web browser component 172 also provides an interface enablingparticipant users to control user profiles and product dataconfidentiality rules, to login and be authenticated into logout of theplatform. The web browser component enables participant users to viewproduct data received from the central server and product data gatheredby the hardware and owned by the participant. In some cases (when theparticipant user has authority to do so) the participant user can alter,correct, enhance, update, and otherwise work with product data,including product data of the participant and product data of otherparticipants. For this purpose, product data of the participant can bestored in the end fetched from the database component 168.

Participant Product Data Acquisition and Delivery Component

The product data acquisition and delivery component 158 is configured tomanage interaction through the communication component 170 with theserver for the purpose of delivering product data that has been storedin the database component 168 or is being generated currently at theparticipant hardware. Product data acquisition and delivery componentalso obtains and receives product data from the central server that hasbeen stored in the database component 120 and processed by variouscomponents at the server. Product data acquisition and delivery can bedone by the product data acquisition and delivery component polling orbeing polled by the server periodically for product data in order tokeep the product data at the participant or the central server, or bothof them, current, complete, and accurate.

Participant Product Data Translation Component

The product data translation component 160 can receive product datadirectly from product data acquisition and delivery component 158 orfrom the database component 168. The received product data can be, forexample, raw product data generated or received by the participanthardware and untranslated according to the canonical format of the kinddiscussed later. The product data translation component 160 can performthe translation of received acquired product data or product data storedin the database component directly and completely into the canonicalformat or can perform a partial translation to be completed a latertime. The result of the operation of the product data translationcomponent will be fully or partially translated product data that isthen stored in the database component. For some purposes, the productdata translation component 160 can perform translation from thecanonical format partially or fully back to a raw (e.g., proprietary)data format used by the participant's hardware.

Participant Product Data Aggregation Component

At the participant's hardware, product data aggregation component canperform similar functions to those described for the product dataaggregation component in the server, with respect to product data of theparticular participant. In some implementations, the product dataaggregation component on the participant's hardware and the product dataaggregation component running on the server can cooperate and share thetasks involved in aggregation. In some cases, performing aggregationtasks on the participant's hardware helps to assure the confidentialityof certain portions of the product data at the source rather thanrelying on the server to perform such aggregation.

Participant Inventory Management System Coordination Component

The inventory management system coordination component 156 coordinatesinteraction with other components, for example, existing components ofthe inventory management systems 154. The other components can includefor example, point-of-sale systems (in the case of retailer productdistribution entities), inventory control systems, order entry systems,shipping management systems, supply-side tracking systems, pricingsystems, and a variety of other systems that control, store, andgenerate product data and information and data associated with productdata or can make use of product data, and analyses of product data.Proprietary inventory management systems will typically require specificinventory management system coordination components designed to, on onehand, be able to interact properly with those proprietary systems, andon the other hand, be able to interact with components specificallyshown in FIG. 3. Therefore, the platform can be seen as including, insome implementations, not only the server and the components running on,but also special purpose components created for and running on existinginventory management systems. The special purpose components can bedistributed electronically and installed by participant users or byrepresentatives of the hosting party.

Participant Database Component

The database component 168 running on the participant hardware includesa database of tables of product data, among other things, and a databaseengine that enables reliable and secure storage and fetching of productdata. The records and fields of the product data tables can be expressedin a wide variety of forms and formats. The stored product data recordscan include records expressed in raw or proprietary product dataformats, such as formats used by inventory management systems running onthe participant hardware, records expressed in accordance with thecanonical format, and records expressed in partially translated formats,or records expressed in other formats, or combinations of them. Thestored product data records can also include records expressed in a widevariety of aggregations based on the parameters described above. Thedatabase component 168 can provide a staging area and in some respects areplication of some or all of the participant's product data that iscommunicated back and forth with the server. In some implementations,the database component 168 and the database component running on theserver can cooperate and share the task of managing portions of theproduct data used by the platform. In some applications, splitting thesetasks can enhance the security and confidentiality of product data of aparticipant.

Participant Analytical Component

The participant analytical component 164 can provide functions andfeatures similar to those described with respect to the analyticalcomponent running on the server. In some applications, the analyticalcomponents at the server and at the participant hardware can splitfunctions, features, and tasks, can replicate functions, features, andtasks, and can operate cooperatively to perform analyses. In somerespects, splitting functions, features, and tasks may enable moreeffective protection of the security and confidentiality of product dataof a given participant. The cooperative operation may also save time andcomputational resources.

Canonical Protocol (Semantic Layer)

As indicated above, a useful feature of some implementations of theplatform is that it translates product data back and forth between (a)varied and incompatible proprietary and other forms, formats, orprotocols of expression used by product distribution entities and otherparticipants and (b) a canonical or common or shared format ofexpression used on the server. By having center format for use withproduct data on the server, a variety of tasks become simpler, easier,more accurate, more timely, and more effective including, for example,storing product data, cleaning up product data, aggregating productdata, and analyzing product data. We sometimes refer to the canonicalor, or shared format as a semantic layer. Said another way, the semanticlayer abstracts into a common format of expression, the disparate waysin which product data and other data are expressed in proprietaryformats, fields, and records, at independent, distributed, proprietaryPOS and other inventory management systems hosted by disparate,independent, sometimes competitive product distribution entities.Significantly, in the aggregate the product data maintained centrally inthe canonical format can represent a comprehensive, universalrepresentation of all of the product data for all products at all stagesof a distribution chain of a product market (or even of an entireconsumer or commercial economy). And the global product data (andaggregations, analyses, portions, or insights about the product data)can be made available to one or more of the product distributionentities along the distribution chain.

Universal Database

Just as the platform can host, maintain, regulate, enforce, andencourage the use of a canonical or common or shared format ofexpression for product data, the platform maintains a database structure(e.g., one or more databases at the central server, at the participants'hardware, or a combination of them) for product data that can representand embody a single universal database of product data across multipleor all product distribution entities, distribution chain entities, andproducts for a product market or multiple product markets. As notedearlier, the database structure can be implemented as a single databaseon the central server or as a distributed database among a set ofservers, or in a variety of other arrangements.

Applications

A wide variety of applications of the platform are possible and useful.Although the platform can be applied to an entire product distributionchain, or even to an entire market for all physical products, typicalapplications focus on particular (and sometimes limited) portions ofdistribution chains having scopes defined by, for example, geography,product category, product model, product manufacturer, time, stage,competitor grouping, and others, and combinations of them.

In some examples, described below, the platform is applied to the stageat which retail products are distributed to retail customers in relatedlocal areas. In some instances, the platform could be applied in asimilar way to other individual stages of distribution chains, such as adistributor stage, a wholesaler stage, a manufacturer stage, or others.The platform could also be applied to combinations of stages of thedistribution chain such as the combination of distributors and theirretailer customers. Virtually any combination of participants could betreated together by the platform to improve the efficiency of theirportion of the distribution chain. For example, all of the wholesalers,distributors, and retailers in a metropolitan area who handle smallkitchen appliances could be treated together as a product distributionsubsystem of interest.

The platform has a wide range of applications in a variety of productmarkets and distribution chains, for a spectrum of product distributionentities, and for various scopes of geography. The applications can alsoextend to participants other than product distribution entities.Applications may be useful in any context in which a productdistribution entity or other participant can acquire or provide productdata or can make use of product data. Especially valuable areapplications in which the product data can be provided or used for thepurpose of efficient inventorying or delivery of products, productordering, supply analysis, prediction of customer demand, prediction ofsupply capability, and other activities associated with efficientoperation of product distribution entities and the distribution chain.

Retail Application

As shown in FIG. 7, here we describe example applications for retailers610 and their retail locations 604, retail products, and retailcustomers 602 (as an example of end consumers). Although the descriptionfocuses on such applications, many of the features described here (andcombinations of them) are also relevant broadly to a wide variety ofother applications. For example, many of the same considerations andtechniques can apply to physical products being distributed tocommercial end consumers in a particular geographic area in a commercialcontext and to the product distribution entities engaged in thatdistribution.

Retailers and Retail Locations

Retailers typically operate as the final stage in a distribution chainfor physical retail products in the sense that the end consumers forretailers are retail customers who are buying retail products to consumethem rather than for further distribution. Putting aside large onlinesites, retailers commonly buy retail products from distributors orwholesalers at upstream stages of the distribution chain and stock themin brick-and-mortar retail locations (e.g., stores) where they are soldand delivered to the retail customers. Normally the retail customersphysically visit the retail locations where they can browse for retailproducts of interest, buy them and take them. In some cases, the retailproducts are delivered from the retail locations to the retail customerswithout the retail customers making such a visit. Both methods can bereferred to as broadly transferring the retail products from the retaillocations to the retail customers.

Because retail customers often visit retail locations, each retaillocation tends to serve a limited local area 606. The local area has aboundary defined so that retail customers situated within the boundarycan reach retail locations by trips that requires no longer than athreshold time or a threshold distance or both (e.g., there is athreshold proximity). Sometimes, a retail customer cannot find a retailproduct she wants at a particular retail location and may then travel toone or more other retail locations within (or beyond) the local area tolocate the retail product.

In some cases, retailers are large entities operating chains of retaillocations across broad geographic areas. In some instances, retailersare small entities operating individual retail locations servingneighborhoods or small locales.

Large Online Retail Sites

Large online retail sites (such as amazon.com, zappos.com andalibaba.com) make retail products easier to find by enabling retailcustomers to shop online, that is, search for, identify, and then buythe retail products without traveling to a retail location. The boughtretail products are then delivered to the retail customer, whichtypically takes a day or two or more. Before delivery, the retailproducts are held in inventory in very large semi-automated warehousesowned or controlled by the large online retail sites. Complete,accurate, and current product data for products at the warehouse can begenerated, maintained, and used to assure that retail productsrepresented by the online site as being available are actually availablefor delivery. By offering a very wide catalog of products, easy andeffective ways to search the catalog online and order the products, andaccurate availability information, the online retail site can satisfyonline retail customers even though delivery of the retail products isnot immediate.

Large online retail sites directly compete with chain retailers, localretailers, and retail locations. Local retail locations are often smallbusinesses serving local communities which make them appealing to nearbyretail customers. Yet retail customers appreciate the very broadselection, convenience, and curation that large online retail sitesprovide. This effect has caused failure of small retail locations inmany communities. Large online retailers empower retail customers, e.g.,based on reviews, price comparisons, and other features. Local stores,by contrast, have been characterized by inefficiencies that also affectupstream stages of the supply chain. These inefficiencies are revealedby key questions: When a retail customer shops in a local retaillocation, how does she know that the price she's paying for the sweatershe's purchasing is the best price in town? How does the local retaillocation know which sweaters to stock and how many of each, includingthe exact colors and sizes, based on the local shopping behavior of thatretail customer (and many like her)? How does the distributor knowexactly which sweaters to supply, how many, and when to deliver them to(fulfill orders for) that specific retail location? How mightdistributors and local retailers cooperate to provide just-in-timedelivery of retail products to local retail locations based on shoppingbehaviors of local retail customers?

Local Retailer Online Sites

To compete with these advantages of the effective online presenceenjoyed by large online retail sites, local retailers sometimes offertheir own limited local retailer online sites associated with theirretail locations. The retail customer of a retail location owned by theretailer can then browse for the product on the local retailer onlinesite to search for and locate products sold by the retailer at aparticular store. The retail customer then can travel to the storeintending to pick up the product. However, the retail customer may findthe product out of stock at the retail location because of incorrectinventory information (product data) supporting the local retaileronline site. After experiencing this situation more than once the retailcustomer may become skeptical of the accuracy of the inventory atparticular retail locations as indicated on the local retailer onlinesite and may resort to the annoyance of calling the retail location toconfirm that a product is available. In any case, because such localretailer online sites are not offered by many retailers, the customercannot compare prices across retailers without going to their respectiveretail locations to see what is available and at what prices.

Data Inaccuracies on Local Retailer Online Sites

The accuracy of a local retailer online site depends on the accuracy ofits product data. The information presented on the local retailer onlinesite may be wrong because the local retailer online site may not beconnected directly to the store's or retailer's inventory managementsystem. Then the inventory listed on the local retailer online site asavailable for sale must be updated manually by the store staff. Shouldthe staff forget to update the inventory regularly to reflect what isavailable or perform the updates incorrectly, or should a particularitem sell out quickly before the staff are able to remove it from thelocal retailer online site, then the list of what is available for salebecomes inaccurate.

Inaccuracies in inventory presented on the local retailer online sitecan also arise when the retail location or the retailer uses severaldifferent, non-integrated, inventory-related applications as part of itsinventory management system. For example, the retail location or theretailer may use one application for managing its inventory and anotherfor its POS transactions. Operating multiple applications for a singleretail location adds to the burden on the store staff to ensure thatinventory information of all applications is consistent, complete,current, and accurate.

What Consumers Want

In other words, large online retail sites typically require retailcustomers to wait for delivery, but their broad selection and otherfeatures undercut the retail customer's interest in patronizing locallyowned retail locations or the retail customer's brand loyalty for localretail locations owned by favored chains; however, the product datapresented on local retailer online sites for local retail locations islimited to particular local retail locations and is often wrong.

At least some retail customers would benefit from access to individualor aggregated local retailer online sites presenting accurate, complete,and current product data for individual retail locations and for a fullrange of separately-owned retail locations within a threshold distanceor threshold travel time of the places where the retail customers aresituated. Such local retailer online sites would enable the retailcustomers to use product-centric keyword searching across a broad anddiverse range of products, identify local retail locations at whichdesired product are reliably known to be available, and then travelimmediately to the retail locations to pick up the products.

Because individual local retail locations tend to be small, to offeronly limited ranges of products, and to suffer from out of stockproblems, achieving that goal would benefit from a system that couldknow, aggregate, and present current product data about the productscarried by and in stock at multiple independent local retailers.

The Local Online Retail Marketplace

In some applications, the platform can host and provide one or morelocal online retail marketplaces for or on behalf of individual localretail locations and aggregations and groups of local retail locationsserving local areas. The marketplaces enable retail customers situatedin those local areas to search for products at individual retaillocations and across multiple retail to locations in the local areas.The retail customers can confidently identify locations at which desiredproducts are known to be available, and immediately (or within a shorttime-frame, such as an hour, three hours, six hours, eight hours, ortwelve hours, as examples) either pick up the products or have themdelivered. The local online retail marketplaces can be served as richfull-functioned user interfaces through browsers on computers or throughmobile applications on mobile devices.

The local areas served by one or more local online retail marketplacescan be defined in a variety of ways. In some cases, the local areas canbe defined based on aggregations of retail locations that aregeographically clustered, such as in business districts, malls, orhighway segments. In some examples, local areas can be defined bygeographic political boundaries, such as towns or counties. In someinstances, local areas can be defined in terms of a threshold distanceor a threshold travel time, or both, from a given location, such as afive-mile radius from the center of a city. Combinations of such localareas can also be used. The area covered by local online retailmarketplaces can be determined dynamically, for example, at the timewhen a retail customer opens a browser window to interact with amarketplace or continuously while the customer is interacting with amarketplace. The area covered can be determined automatically withoutparticipation by the retail customer or the retail customer can selectthe area or express preferences that affect the area covered.

Sometimes local areas can be defined for each retail customerindividually, such as an area within a threshold distance or thresholdtravel time from the specific retail customer's apartment building. Inother words, the user interface presented to each retail customer can be(but need not be) unique to that customer in presenting informationabout products available in local retail locations that are within athreshold distance or threshold travel time determined for thatcustomer.

A variety of definitions can be used for the threshold time (say, 10minutes, 30 minutes, or an hour, to name three examples, as well as theexamples previously noted) or for the threshold distance (say, one-halfmile or one mile or 5 miles or 50 miles, or other distances). The localonline retail marketplaces can enable a retail customer to choose orspecify a threshold time or a threshold distance or ranges of them orcombinations of them as a way to affect the local area covered by agiven local online retail marketplace.

There can be one or more local online retail marketplaces provided bythe platform. In some implementations, a single local online retailmarketplace would be available online to all retail customers for alllocal areas in a country or state or province, for example. Thisarrangement would allow a retail customer to choose the local area ofinterest. In some cases, the platform uses product data to servemultiple local online retail marketplaces for use by respective groupsof retail customer users depending on their locations. In someapplications, as noted above, each retail customer would be served apersonalized local online retail marketplace based on her location andother personalization and demographic factors such as product categoriesof interest, age, gender, income, and others. The platform could enablea retail customer to adjust the content and presentation of apersonalized local online retail marketplace based on expressedpreferences.

In some cases, the platform could serve only a single local onlineretail marketplace for a particular retail location at a time. Theretail customer could navigate from one such local online retailmarketplace to another within a local area to browse for and findproducts of interest. In some instances, the platform could serve localonline retail marketplaces that are not based on retail location butrather are focused on a local area. For example, the platform couldenable a retail customer to use key word searching for productsavailable at any participant retail location within a defined localarea. When results of the search are displayed, for example, for KitchenAid food processors, the retail customer could then be shown the namesand addresses of the retail locations within the threshold proximitywhere the product is known to be available.

To be able to present such local online retail marketplaces, theplatform acquires, organizes, stores in the database component 120, andaggregates product data for more than one (e.g., many or all) of theretail locations in a given local area in a manner to keep the productdata current, accurate, and complete.

In effect, the platform uses deep and broad available product dataacross competitive retail product distribution entities to enable retailcustomers to shop for available products at one or more (or all) retaillocations in a relevant local area (for example a local area within adetermined or selected threshold proximity). In that way, the platformcan present a breadth and depth of retail product inventories comparableto those offered by centrally inventoried product distribution entitiessuch as Amazon. And the platform can identify retail locations at whichthe products are available within a threshold proximity that makestransfer of the products to the retail customers within a matter ofminutes or hours or miles feasible and convenient.

An additional significant aspect and benefit of the platform is inenabling upstream product distribution entities, such as wholesalers ordistributors 608, to have knowledge of the in-stock state (andpredictions 620 of the future in-stock state) of some or all productsfor some or all local retail locations in a local area. Said anotherway, the platform makes more efficient and effective fulfillmentpossible. Distribution of products (fulfillment 622) from upstreamdistribution stages to retailer locations can be done at the right timesand in the right quantities to match predicted in-stock states. Insteadof being held in inventory by the wholesaler or distributor until nolonger in stock at its customer retail locations, products can bedistributed to the final stage of the distribution chain (the retaillocations) in time to maintain in-stock states. Then because retaillocations are essentially never in an out-of-stock state for anyproduct, assured elapsed times for transfer of the products to theretail customers can be reduced to minutes or hours rather than days.

With all in-demand products being distributed to and available at theretail locations in the local area, in some implementations, a deliveryservice can provide delivery directly from the retail locations to thelocations of the retail customers within a threshold time. The hostingparty of the platform could partner with a last-mile distributionprovider (e.g., Uber Eats, Postmates, Lyft, USPS) to move products fromthe retail locations to retail customers. Retailer locations could alsomail products directly to retail customers or be directly responsiblefor delivery (similar to a local pizza shop). And retail customers couldtravel to the retail locations for immediate pickup of products.

Similar activities and advantages can be achieved between upstreamstages of the distribution chain using similar hardware and software.For example, the platform could provide relevant current product dataand demand predictions for manufacturers with respect to current andpredicted in-stock states of their wholesalers and distributors.

Based on the product data provided by the participants of the platform,the central server of the platform always maintains or has access toaccurate, complete, and current product data reflecting the inventory instock and available for delivery at each of the physical retaillocations of all of the retail platform participants, for example,within a given local area. In addition to being used to support thelocal online retail marketplaces, the product data held in the centralserver of the platform can be used to predict demand for products in thelocal area and be made available to other participants for a variety ofpurposes.

Analysis and Insights

In addition to making quick-delivery purchases by retail customers inlocal areas easy, the local online retail marketplaces hosted by theplatform can create value by aggregating, analyzing, deriving insightsabout retail customer behavior, and providing product data, aggregatedata, predictions 620 of demand, analytics 616, and insights 618(including market basket insights) to participants. Market basketinsights include, for example, information about which retail customersin a local area buy which products, in which combinations, and in whatquantities, and how different products and groups of products areincluded in the purchasing habits of consumers in a local area.

Participants who may buy or otherwise receive such product data or otherdata, predictions, analysis, and insights include retailers, retaillocations, and other product distribution entities in the distributionchain. For example, such data, analysis, and insights sold on asubscription or a transactional basis can be valuable to retailers whodo not maintain their own local online retail marketplace (sometimescalled “offline retailers”). Based on such data, predictions, analysis,and insights, offline retailers can make better determinations of whichproducts to stock at their retail locations. Distributors andwholesalers also can benefit from acquiring such data, predictions,analysis, and insights by using them to determine when to deliverparticular products to their retailer customers (fulfillment).Manufacturers, in turn, can determine which products to produce and inwhich quantities for supply to their distributor and wholesalercustomers. Using such techniques, costs can be kept lower for the retailcustomers, retailers, and other product distribution entities in thedistribution chain. The retail locations, retailers, and other productdistribution entities can access the data, analytics, insights, andpredictions through online dashboards served from the platform.

Benefits of the Local Online Retail Marketplaces

The local online retail marketplaces provide at least the followingother advantages.

By acquiring product data for many or all POS and other inventorymanagement systems of retailers and retail locations (includingpreviously offline retailers) in one local area, groups of local areas,or all local areas, the platform and its local online retailmarketplaces allow retail customers to search, compare products, andorder across the available products of those retail locations inreal-time. The local online retail marketplaces can provide the consumeras much convenience and curation as traditional large online retailsites (e.g. Amazon). Unlike the large online retail sites, the productsbeing shopped for are known to be available at retail locations in thelocal areas, or are otherwise available for pickup or delivery,immediately or within a brief period.

By providing retailers (and other product distribution entities) withproduct data, predictions, analyses, and insights into behavior ofretail customers within a local area, the retailers will have thecapability to stock their shelves with the exact products that matchlocal retail customers' needs and wants. The local online retailmarketplaces will collect, aggregate, and anonymize product dataindicative of which products local retail customers shop for and howthey shop for them. By providing that product data, predictions,analyses, and insights back to retailers in a useful format (e.g.,through a real-time dashboard), offline retailers will have the samecapabilities to curate and match products to local retail customerdemand as traditional large online marketplaces have done. By enablingsuppliers to provide just-in-time delivery, the platform ensures thatretail customers' favorite products will be in stock based directly onknown retail customers' shopping behavior. In essence, the platform canenable a completely efficient distribution chain based on product dataabout real-time retail customer behavior across different incompatiblePOS and other inventory management systems, retailers, and geographies.

The platform enables the retailer to determine which products to selland when and at which retail locations to sell them to retail customersin a local area, enables the supplier or wholesaler to determine whichproducts to deliver and when and to which retail locations to deliverthem across its retailer customers, e.g., a set of retailers, andenables the manufacturer to know which products to make and when andwhere to make them. Using real-time integration of received productdata, the platform provides local online retail marketplaces throughwhich otherwise offline retailers can operate online without effort.

Significantly, the product data, analyses, and insights provided by theplatform through the dashboards are driven from and based ontransactions at the final stage of the distribution chain, that is,information derived from the retail customers. The platform thenprovides corresponding product data at upstream stages of thedistribution chain in real-time. The platform transforms existingproprietary and separate POS and other inventory management systems intohubs of product data and thus transforms what was once a fragmented andinefficient offline retail stage of the distribution chain into anefficient and transparent online retail stage. Retail customers will nolonger miss out when a favorite item appears on retail location shelves.Retailers will no longer need to carry overstock items that don't sell.Suppliers will no longer need to operate in the dark about the retailperformance of their products.

Incentives to Participation

Product distribution entities will choose to be participants in theplatform and to buy real-time product data, analytics, insights, andpredictions from the platform for at least the following reasons. Byhaving access to real-time product data (e.g., inventories) of multipleretailers and retail locations, including purchase rates, productdistribution entities can provide just-in-time delivery of products(fulfillment) to retailers and retail locations. By having access toreal-time pricing data of a given product (or groups of similarproducts) across multiple retailers aggregated for a local area, productdistribution entities will know how to dynamically price their productsto the retailers. Product distribution entities can obtain insights notonly into what products to distribute, but also into how, when, by whom,and in what quantities products are being consumed. The insights canalso include which products are complements of one another orsubstitutes of one another and which sub-categories of products are morepopular than other sub-categories in a local area.

Manufacturer participants in the platform can receive real-time productdata about aggregated retail customer shopping behavior at retaillocations across multiple local areas. With these insights,manufacturers can determine what products to make and when to make thembased on the historical, real-time, and predicted shopping behavior ofretail customers across different local areas.

The frictional cost for an end consumer (e.g., a retail customer)associated with finding a product quickly and nearby has enabled largeonline retail sites to build large retail businesses by having a broadrange of products in stock at central warehouses and being able to getthem to the customer within two days, for example. A holy grail forretailing is to be able to offer a complete, broad, and deep range ofproducts and be able to deliver them or have them picked up locallywithin minutes or hours instead of days. The platform and the localonline retail marketplaces facilitate this result at the retail stage ofthe product distribution chain. Similar uses of the platform can providesimilar advantages at other stages of the distribution chain.

In some implementations, the platform and the local online retailmarketplaces could eliminate the need for some kinds of physical retaillocations in contexts in which retail customers do not need to browse ina retail location for products of interest. Such retail locations couldbe replaced with retail locations in lower rent districts yet within alocal area. Such retail locations could take the form of local inventorylocations from which pickup or delivery could be managed for a singleretailer or across multiple competitive retailers and retail locations.A given retailer or retail location could specialize in a class ofproducts and be situated in a lower-cost location rather than having awalk-in showroom in a higher cost location. Retail customers could go toa local inventory hub location for express pick-up of products, use adrive-through to receive the products, or have the products delivered.

No Sharing by Local Retail Locations of Product Data

In theory, retail locations in a local area could cooperatively anddirectly share their product data among themselves and with suppliersand wholesalers to achieve some of the benefits of the platform and thelocal online retail marketplaces. Yet, in reality, because they compete,the retail locations have motivations not to cooperate, for example, insharing product data that captures details about retail customerbehavior. This reluctance to share product data hampers efforts toanalyze and develop insights on retail customer shopping behavior, bothat an individual retail customer level and at an aggregated marketlevel.

To fill in this gap of shared product data, some retail analyticscompanies attempt to collect and aggregate retail customer behavior datafrom individual retailers and retail locations. Doing so is hard andexpensive because the analytics companies must visit many differentretail locations and negotiate separate agreements to obtain theirproduct data. In some cases, the retail locations and retailers may notbe collecting the needed product data, forcing the retail analyticscompanies to install data collection systems. The collected data may notbe updated in real-time making some of it inaccurate.

Retailer and retail location participants in the platform and the localonline retail marketplaces and dashboards may be motivated by theability to share real-time product data through the platform with theirdistributors or wholesalers in exchange for access to fully-automatedlocal online retail marketplaces, just-in-time inventory fulfillment,and the ability to know exactly what products and the number of units ofeach product to stock on retail location shelves for the population ofretail customers in the local area (as well as where and when to stockthose products).

For example, access to the product data could be given to a distributorof one or more retailer participants. Such a distributor might otherwisehave to build its own component of an inventory management system totrack the inventory of its customers to predict the retailers' demandfor products. Instead, the distributor's hardware can run simple datacommunication components enabling it to access the dashboards or otherfacilities of the platform and have immediate access to accurate,complete, and current product data across all of the inventorymanagement systems of the retailers and retail locations that are ormight be customers of the distributor. Retailer customers of thedistributor are benefited because the distributor can plan its owninventory and manage deliveries of products to the retail customers(fulfillment) in the right numbers of units and at the right times.

Trust

The collection and distribution of product data by the platform and itslocal online retail marketplaces conform to tenets of trust, value, andtransparency. The platform will establish trust with end consumerparticipants by providing convenience (e.g., for end consumers who areretail customers, “The local online retail marketplaces can provide mewhatever product I want locally whenever I want it, without the delaytypical of online retail sites”) and curation (e.g., “On the localonline retail marketplaces I can find the product I want, at the price Iwant to pay, from the retailer I want to patronize, and get it rightaway.”).

For retailer and retail location participants, the platform and thelocal online retail marketplaces earn trust by offering sought afterfunctions and features. By providing retailers with turnkey local onlineretail marketplaces that are essentially free to use andproduct-data-supported insights that enable the retailers to decide whatproducts to sell and how to sell them, the platform establishes trustwith the retailers and gains permission to access their private productdata. Retailers who don't become participants in the platform stand tolose invaluable product data, analyses, insights, and predictions forretail customer behavior in the local area, be left to build their ownonline retail sites, and potentially lose access to their real-timelocal retail customer base. Conversely, by participating in the platformand the local online retail marketplaces retailers gain valuable productdata, analyses, insights, and predictions, operate an automated (andfree) best-in-class local online retail marketplace, and can presentavailable products directly to online retail customers actively shoppingfor those products.

Value is created not only for retailers and retail locations, but alsofor wholesalers, suppliers, and manufacturers. By having a majority (ifnot all) retailers and retail locations as participants in the platform,the product data captured will become valuable to many productdistribution entities. The platform can charge suppliers andmanufacturers and other participants for access to real-time productdata, analyses, insights, and predictions at the level of individualretail locations.

The platform operates transparently to each participant across thedistribution chain. For every participant, such as an end customer or aretailer, the platform will reveal what data is being accessed, how it'sbeing utilized, and why that's important for them. For instance, aretail customer will understand that by sharing his or her shoppingbehavior, the platform can curate and suggest new and better products.The retailer will know that sharing its product data will allow theplatform to ensure that a best-selling product never runs out of stock.

At the platform, product data can be processed to aggregate andanonymize market basket data. In some implementations, retailers,suppliers, and manufacturers, among other participants, have no accessto product data identifying how an individual retail customer shops, butwill be able to see how an anonymized group of individual retailcustomers shop. The market basket data can be aggregated at variousgeographic levels corresponding to local areas (e.g., by zip code, city,state, or country or in other areas as discussed earlier.). The productdata shared with suppliers can be provided at an individual retail levelor aggregated and anonymized at various geographic levels. For instance,the supplier could see product data revealing which products are runninglow in inventory (getting close to an out-of-stock state) at a specificretailer's location or at a level of a local area or regionally. Inaddition, the platform can provide product data that enables suppliersto deliver products just-in-time, potentially enabling an optimallyefficient distribution chain extending from the manufacturer to theretail customer, based on real-time product data indicative of retailcustomer behavior.

A variety of techniques can be used on the platform to protectconfidential product data and other information associated withparticipants.

In some examples, to acquire the product data, components running on thecentral server will poll components running on the participants'hardware to obtain the real time product data (e.g., inventory levels ortransactions) and other product data for all products at each retaillocation and store that information in the database. Each retailerparticipant will own the data for its retail locations and will grantthe hosting party of the platform a non-revocable license that allowsthe hosting party to aggregate, analyze, and distribute the data. At anytime, the retailer participant may opt out of the product datacollection. With respect to access, no entity outside the hosting partywill have access to the database. The platform will share product datafrom the database and other information either using a report mode orusing an authorized API for electronic delivery.

Participant connections to postgres databases require SSL encryption toensure a high level of security and privacy. When deployingapplications, the platform will encourage participants to take advantageof encrypted database connections.

Product data can be encrypted by customer applications to meet datasecurity requirements. Customers can implement data storage, keymanagement, and data retention requirements when developingapplications.

Access by third party participants in the platform to product data,analyses, and insights can be done by a report produced by a participantAPI. The API can require SSL encryption and an access-controlled token.The token will only provide access to permitted information for theparticipant (there are no global tokens with access to all). The host ofthe platform controls the access-control tokens and can enable ordisable them.

Implementations

As shown in FIGS. 4 and 5, in some implementations of the platform,polling software 408 running at the server keeps an accurate account ofall inventory (product data) counts across all retail locations and tiesthose inventory counts with product and brand representations in aproduct table 514 and a brand table 518. The local online retailmarketplace (e-commerce platform) 414 tracks purchasing behavior atretail customer, retail location, market, and industry levels. Inventoryanalytics software 424 running at the server uncovers and predictspurchasing behavior based on (but not limited to) location, product,brand, category, subcategory, and customer base. This enables theplatform to collect complete, accurate, timely product data frompotentially all of the product distribution entities, delivery entities,and other parties associated with the distribution chain (e.g., all ofthe participants in the platform). The platform enables easy andpredictive fulfillment of products by product distribution entities forretail locations based on analyzed consumption. Among other things, theplatform can connect product distribution entities that distributespecific brands with retail locations that are looking to restock thosebrands based on real-time consumption of their retail customers.

Example Database Records

In some implementations, the product data can be maintained as recordsin the database component 410 of the server. In some cases, the fieldsof the product data records of the database could include at least thefollowing:

-   -   retail location_id: Unique identifier for the retail location.    -   product_id: Unique identifier for the product within the        platform; each product is assigned a unique identifier.    -   inventory_count: A current count of the units of the product at        the retail location.    -   time stamp: The time when the count of units of the product was        made.    -   location stamp: The location of the retail location, for        example, its geographic coordinates.    -   product stamp: An identification of a product model or a product        unit.

Polling and Product Mapping Software

As a retail location sells products in inventory (in an in-stock state)to retail customers 416, the polling and product mapping software 408will update the inventory items associated with those products in aninventory table 508 as stored in a central database 410. The softwareintegrates with the retail location POS systems 402, 404, 40 directly orindirectly through an API call or similar method to poll all activeinventory (product data) at the retail locations. The polling softwareuses custom drivers to interpret the polled active product datainventory and then map (translate) the polled inventory items to theircanonical product representations in the product table 514. The pollingsoftware uses a proprietary string distance algorithm to infer to a highprobability the most likely canonical product representation in theproduct table 514. Once a match is determined the polling softwarecreates or updates the inventory (product data) entry in the inventory(product data) table 508 through an API using the polled information. Asa result, the records of the inventory table 508 reflect each retaillocation's active inventory counts (product data) and the table matchesthe inventory items (products) with their canonical productrepresentations. All of these tables are stored in the central database410. The polling software operates continuously to update the inventoryproduct data to reflect the current actual inventory counts at theretail locations.

The mapping of inventory items (units of in-stock products) at theretail locations to their canonical product representations in theproduct table 514 enables any software, API, or other product dataconsuming entity or component connecting to the central database 410 notonly to view the inventory counts, but also to match those inventoryitems with a standardized product library with inherited content in theproduct table describing the product. Content (e.g., fields of theproduct table) can include but is not limited to: name, category,sub-category, brand, attributes, SKU, description, and photos. Thevalues for these fields are consumed and served to the local onlineretail marketplace and any other application or API coupled to thecentral database 410.

Consumption Analytics

Using real-time inventory product data, product descriptions, and cartdata as powered by the polling software 408 and local online retailmarketplace 414, analysis of the product data can be done at the levelof a customer, a location, a market, or an industry, for example. Theseanalytics 418 can uncover and predict purchasing behavior based on butnot limited to location, product, brand, category, subcategory, andcustomer base.

Retail locations 422, 428 can use the results of the analytics to decidewhich products to carry based on predicted consumption.

Other product distribution entities 422, 426 use the results of theanalytics to decide which products to manufacture, produce, ordistribute, for example. based on retail customer consumptioninformation reflected in the product data.

Inventory Analytics

Utilizing the actual current inventory, product, and cart data asreceived by the polling software and the local online retailmarketplace, inventory analytics 424 can be done at a level of acustomer, a location, a market, and an industry. These analytics canuncover and predict purchasing behavior based on but not limited tolocation, product, brand, category, subcategory, and customer base.Product distribution entities can use the results of these analytics toengage in “consumption based predictive fulfillment”, that is, deliveryof retail products at times and locations suggested by predictions ofconsumption by retail customers.

The distribution table 520 of the database component locations stores arecord for each product distribution entity that will be notified ofproducts that will need to be re-supplied or presented to retaillocations for corresponding products that are predicted to requirefulfillment (e.g., restocking) at those locations at identified times.The inventory analytics software notifies the product distributionentities of the inventory items (products and counts of products) thatare expected to be out of stock based on predicted consumption. Thoseinventory items are associated with products in the product table 514.The products are associated with brands in the brand table 516, andthose brands are associated with product distribution entities thatdistribute those products in the distribution table 520 and brands aslocated in the distribution brand table 518.

E-Commerce Platform

The e-commerce platform 414 (e.g., a local online retail marketplace) issupported by the server components 412, the database at the centralserver 410, and product data acquired by the polling software 408. Thee-commerce platform can provide an accurate current menu of products andproduct counts at retail locations that are participants. The menu canbe presented to retail customers through web pages of the local onlineretail marketplace enabling retail customers to create carts and placeorders at participant retail locations. The products contained in theonline carts are tracked and stored in the cart table 504 in thedatabase of the central server. Each record in the cart table containsone or more cart_inventory entries that represents a purchased product.

In some implementations, the database component 410 is a cloud-hosteddatabase at the server and is connected to other software and APIsthrough the server components 112 that are realized as a cloud hostedapplication. The server components 112 handle incoming queries forproduct data and other information in the tables of the database andcreates, reads, updates, and destroys the necessary data in the tablesof the database.

The database contains at least the following tables:

Product Table

The product table 514 contains a record for each unique product handledby the platform. A product can be, for example, any product can be abought at a retail location and is associated with: a brand (e.g., amanufacturer of the product such as Kiva or Korova), a product category(e.g., edible, flower, tincture), and a product sub-category (e.g.,baked goods, candies).

Product objects (e.g., records) contain at least the following fields:

-   -   id (integer): An id of the product, unique within the entire        platform.    -   brand_id (integer): An id associated with a brand.    -   category_id (integer): An id associated with a category.    -   sub_category_id (integer): An id associated with a sub-category.    -   name (string): A name of a table (e.g., Chocolate Chip Cookie,        Watermelon Gummies).    -   attributes (string list): A list of product attributes (i.e. 100        mg THC, 4 Pack)    -   SKU (string): A globally unique SKU if available.    -   description (string): A description of the product.    -   photos (list string): A list of product photo URLs.

Category Table

Each of the records in the category table 506 defines category ofproducts uniquely across all categories of products in the platform. Insome implementations, a category is the top-level classification of aproduct. A category object (record) contains at least the followingfields:

-   -   id (integer): An id of a category unique within the entire        platform.    -   name: A name of a category.

Sub-Category Table

Each record in the sub-category table 512 represents an individualunique sub category of products handled by the platform. A sub-categoryis a low-level classification of a product. A sub-category object(record) contains at least the following fields:

-   -   id (integer): A unique id of a sub category within the platform.    -   name: A name of a sub category.

Brand Table

Each record of the brand table 516 represents an individual uniquecategory (brand, manufacturer) in the platform. A brand represents themanufacturer of a product. Brand objects (records) contain at least thefollowing fields:

-   -   id (integer): The unique id of the brand within the platform.    -   name: The name of the brand.

Distribution Table

Each record of the distribution table 520 represents a productdistribution entity. Examples of product distribution entities include:manufacturers, distributors, wholesalers, and retailers, among others.Distribution objects (records) contain the following information:

-   -   id (integer): A unique id of a product distribution entity        within the platform.    -   name (string): The name of the product distribution entity.

Distribution_Brand Table

Each record of the distribution_brand table 518 represents arelationship between a product distribution entity and a brand. If aproduct distribution entity manufactures, supplies, or distributes abrand, the relationship will be stored in this table. Distribution_brandobjects (records) contain at least the following fields:

-   -   distribution_id (integer): This id refers to a distribution        entry in the distribution table.    -   brand_id (integer): This id refers to a Brand entry in the Brand        table.

Location Table

A record of the location table 510 describes a retail location in theplatform. A retail location is, for example, a location that sellsproducts to retail customers. Location objects contain the following:

-   -   id (integer): The unique id of the location within the platform.    -   address (string): The physical address of the location.    -   latitude (double): The latitude of the location.    -   longitude (double): The longitude of the location.

Inventory Table

A record of the inventory table 508 represents an inventory item in thesystem. An inventory item is, for example, a product in a retaillocation's inventory. The inventory object (record) contains at leastthe following fields:

-   -   id (integer): A unique id of an inventory item within the        platform.    -   location_id (integer): An id referring to a location entry in        the location table.    -   product_id (integer): An id referring to an entity entry in the        entity table.    -   inventory_count (integer): A count of a current active inventory        at a retail location.    -   updated_at (datetime): The last time the inventory item was        updated.    -   price (double): The listed price of the inventory item at the        retail location.    -   pos_id (double): The identifier of the inventory item in the        retail location's POS system.

Cart Table

Each record of the cart table 504 represents a cart. A cart is apurchased or pending purchase at a retail location. The cart object(record) contains at least the following:

-   -   id (integer): The unique id of the cart within the platform.    -   location_id (integer): This id refers to a location entry in the        location table.    -   created_at (datetime): The date and time the order was placed.    -   customer_id (integer): The unique id of the customer that        created the cart.

Cart_Inventory Table

Each record of the cart_inventory table 502 represents a relationship ofa cart to an inventory item in the platform. A cart_inventoryrelationship represents a purchased item in a cart. The cart_inventoryobject (record) contains the following:

-   -   id (integer): The unique id of the cart-product relationship        within the platform.    -   inventory_id (integer): An id referring to an inventory entry in        the inventory table.    -   quantity (integer): The amount purchased in the cart.    -   created_at (datetime): The date and time the order was placed.    -   cart_id (integer): The unique id of the cart entry in the cart        table.

The Process Flow

In some implementation, the flow of the platform with respect toproducts at retail locations includes at least the following activities.

-   -   1. Product data is obtained frequently by polling software        (e.g., components) running on the participant's hardware or the        server or a combination of them.    -   2. The acquired product data is converted to a canonical format        at the participant's hardware, at the central server, or at a        combination of them.    -   3. The translated product data is stored in the server database        according to the canonical format and the product taxonomy.    -   4. The product data is processed and analyzed. For example,        product data can be aggregated according to geography.    -   5. The product data and insights are then made available to        participants free or by subscription.

Some implementations of the components running on the retailer'shardware and interacting with the server could be configured inaccordance with the description in U.S. Pat. No. 9,836,772 (incorporatedhere by reference in its entirety).

User Interfaces

As discussed earlier, the platform can provide user interfaces, webpages, and dashboards for retail customers, retailers, and other productdistribution entities in the distribution chain. The user interfaces forretail customers can be local online retail marketplaces.

The user interfaces for retailers can be in the form of a real-timedashboard, from which real-time product data (e.g., inventory) andcorresponding analyses and insights can be viewed and manipulated fromany Internet-capable device. The retailer user interface will allowretailers to order directly from suppliers (or set scheduled orders ofproducts based on real-time inventory thresholds) and perform a varietyof other functions. (e.g., de-list a product from its online retailsite, assign a discount to a product, and other functions).

The user interface for suppliers allows suppliers to receivenotifications, track and fulfill orders from individual (or groups of)retailers and retail locations, and view analyses and insights intoshopping behavior within one or more local areas in real-time. Using areal-time dashboard from any Internet-capable device, the supplier canview the real-time inventory of every product it distributes at eachretail location to which it delivers products. Suppliers can schedulejust-in-time deliveries and learn about the market basket data for eachlocal area, enabling the suppliers to determine how best to bundleproducts, what categories or sub-categories in which to expand theirproduct lines, how to price their products, and where best to sell them.The supplier dashboard also will allow distributors to shop for productsdirectly from manufacturers.

The user interface for manufacturers is a real-time dashboard that canbe accessed from any Internet-capable device. In addition to receivingnotifications of, tracking, and fulfilling orders from suppliers,manufacturers have access to real-time consumption and supplier productdata. From this information the manufacturers can determine whichproducts are selling in respective local areas and in what quantities.This information enables manufacturers to plan just-in-time productionof those products (or raw materials, substitutes, or complements ofthose products).

The platform can implicitly coordinate the activities of all productdistribution entities along a distribution chain. For example, based onthe purchase behavior of retail customers within a local area, theretailer can determine what to offer for sale, the supplier candetermine what to supply, and the manufacturer can determine what tomake.

Third party participants 4242 can provide information and accessinformation through a participant API 4141 communicating with the servercomponents.

Other implementations are also within the scope of the following claims.

The invention claimed is:
 1. A method comprising: repeatedly receiving,by a server of a platform and from devices associated with retaillocations, transaction records having elements including combinations ofproducts purchased by individual customers in respective individualtransactions with retail locations that are competitors, the transactionrecords having aspects confidential to the retail locations, theplatform being controlled independently of the retail locations, thedevices associated with the retail locations identifying products usingvaried and incompatible proprietary formats, and the transaction recordsbeing in multiple of the proprietary formats; translating, by the serverof the platform, the transaction records from the multiple proprietaryformats that are associated with the devices of the retail locationsinto a canonical format that is in shared use by the server of theplatform and that provides a respective universal representation foreach of the products; storing, by the server of the platform and in adatabase, the transaction records that are in the canonical format thatis in shared use by the server of the platform, including storing theconfidential aspects; controlling, by the server of the platform, accessto protect the confidential aspects of the transaction records that arein the canonical format that is in shared use by the server of theplatform; processing, by the server of the platform, the transactionrecords that are in the canonical format that is in shared use by theserver of the platform to produce derived information that does notreveal at least some of the confidential aspects of the transactionrecords, the derived information comprising market basket informationrepresenting purchasing behavior of the retail customers with respect tocombinations of the products purchased in respective individualtransactions with the retail locations, the derived information being inthe canonical format that is in shared use by the server of the platformand identifying products in each combination by their respectiveuniversal representation; for a particular retail location, translating,by the server of the platform, the derived information from thecanonical format that is in shared use by the server of the platform toa particular proprietary format that is associated with devices of theparticular retail locations; and communicating, by the server of theplatform, the derived information in the particular proprietary formatthat is associated with the devices of the retail locations to theparticular retail location.
 2. The method of claim 1, wherein processingthe transaction records comprises predicting demand for the products. 3.The method of claim 1, wherein processing the transaction recordscomprises aggregating the data to produce aggregated data free of theconfidential aspects.
 4. The method of claim 1, wherein processing thetransaction records comprises analyzing consumer demand behavior.
 5. Themethod of claim 1, wherein processing the transaction records comprisesanalyzing supply.
 6. The method of claim 1, wherein controlling accessto protect the confidential aspects of the transaction records comprisesexecuting a confidentiality enforcement component at the server.
 7. Themethod of claim 1, wherein producing the derived information comprisesaggregating elements of the transaction records according to at leastone of time periods, geographical areas, product categories, productmanufacturers, and product numbers.
 8. The method of claim 1, whereinthe communicating the derived information comprises communicatingthrough an authorized API.
 9. A non-transitory computer readable storagemedium storing instructions executable by a data processing apparatusand upon such execution cause the data processing apparatus to performoperations comprising: repeatedly receiving, by a server of a platformand from devices associated with retail locations, transaction recordshaving elements including combinations of products purchased byindividual customers in respective individual transactions with retaillocations that are competitors, the transaction records having aspectsconfidential to the retail locations, the platform being controlledindependently of the retail locations, the devices associated with theretail locations identifying products using varied and incompatibleproprietary formats, and the transaction records being in multiple ofthe proprietary formats; translating, by the server of the platform, thetransaction records from the multiple proprietary formats that areassociated with the devices of the retail locations into a canonicalformat that is in shared use by the server of the platform and thatprovides a respective universal representation for each of the products;storing, by the server of the platform and in a database, thetransaction records that are in the canonical format that is in shareduse by the server of the platform, including storing the confidentialaspects; controlling, by the server of the platform, access to protectthe confidential aspects of the transaction records that are in thecanonical format that is in shared use by the server of the platform;processing, by the server of the platform, the transaction records thatare in the canonical format that is in shared use by the server of theplatform to produce derived information that does not reveal at leastsome of the confidential aspects of the transaction records, the derivedinformation comprising market basket information representing purchasingbehavior of the retail customers with respect to combinations of theproducts purchased in respective individual transactions with the retaillocations, the derived information being in the canonical format that isin shared use by the server of the platform and identifying products ineach combination by their respective universal representation; for aparticular retail location, translating, by the server of the platform,the derived information from the canonical format that is in shared useby the server of the platform to a particular proprietary format that isassociated with devices of the particular retail locations; andcommunicating, by the server of the platform, the derived information inthe particular proprietary format that is associated with the devices ofthe retail locations to the particular retail location.
 10. The mediumof claim 9, wherein processing the transaction records comprisespredicting demand for the products.
 11. The medium of claim 9, whereinprocessing the transaction records comprises aggregating the data toproduce aggregated data free of the confidential aspects.
 12. The mediumof claim 9, wherein processing the transaction records comprisesanalyzing consumer demand behavior.
 13. The medium of claim 9, whereinprocessing the transaction records comprises analyzing supply.
 14. Themedium of claim 9, wherein controlling access to protect theconfidential aspects of the transaction records comprises executing aconfidentiality enforcement component at the server.
 15. The medium ofclaim 9, wherein producing the derived information comprises aggregatingelements of the transaction records according to at least one of timeperiods, geographical areas, product categories, product manufacturers,and product numbers.
 16. The medium of claim 9, wherein communicatingthe derived information comprises communicating through an authorizedAPI.
 17. A system comprising: one or more processing devices; and one ormore storage devices storing instructions that are executable by the oneor more processing devices to perform operations comprising: repeatedlyreceiving, by a server of a platform and from devices associated withretail locations, transaction records having elements includingcombinations of products purchased by individual customers in respectiveindividual transactions with retail locations that are competitors, thetransaction records having aspects confidential to the retail locations,the platform being controlled independently of the retail locations, thedevices associated with the retail locations identifying products usingvaried and incompatible proprietary formats, and the transaction recordsbeing in multiple of the proprietary formats; translating, by the serverof the platform, the transaction records from the multiple proprietaryformats that are associated with the devices of the retail locationsinto a canonical format that is in shared use by the server of theplatform and that provides a respective universal representation foreach of the products; storing, by the server of the platform and in adatabase, the transaction records that are in the canonical format thatis in shared use by the server of the platform, including storing theconfidential aspects; controlling, by the server of the platform, accessto protect the confidential aspects of the transaction records that arein the canonical format that is in shared use by the server of theplatform; processing, by the server of the platform, the transactionrecords that are in the canonical format that is in shared use by theserver of the platform to produce derived information that does notreveal at least some of the confidential aspects of the transactionrecords, the derived information comprising market basket informationrepresenting purchasing behavior of the retail customers with respect tocombinations of the products purchased in respective individualtransactions with the retail locations, the derived information being inthe canonical format that is in shared use by the server of the platformand identifying products in each combination by their respectiveuniversal representation; for a particular retail location, translating,by the server of the platform, the derived information from thecanonical format that is in shared use by the server of the platform toa particular proprietary format that is associated with devices of theparticular retail locations; and communicating, by the server of theplatform, the derived information in the particular proprietary formatthat is associated with the devices of the retail locations to theparticular retail location.
 18. The system of claim 17, whereinprocessing the transaction records comprises predicting demand for theproducts.
 19. The system of claim 17, wherein processing the transactionrecords comprises aggregating the data to produce aggregated data freeof the confidential aspects.
 20. The system of claim 17, whereinprocessing the transaction records comprises analyzing consumer demandbehavior.
 21. The system of claim 17, wherein processing the transactionrecords comprises analyzing supply.
 22. The system of claim 17, whereincontrolling access to protect the confidential aspects of thetransaction records comprises executing a confidentiality enforcementcomponent at the server.
 23. The system of claim 17, wherein producingthe derived information comprises aggregating elements of thetransaction records according to at least one of time periods,geographical areas, product categories, product manufacturers, andproduct numbers.
 24. The system of claim 17, wherein communicating thederived information comprises communicating through an authorized API.